A revised simplex method for linear multiple objective programs

Evans, James P.; Steuer, Ralph E.

doi:10.1007/bf01580111

Cited by 229 publications

(87 citation statements)

References 4 publications

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“…We will later uae MCLP to define connectednese in discrete multiple criteria optimization. Before the general result of Theorem 1 was known the connectedness result for MCLP had been proved by varioua authora [2,4,12]. The most important solutions in linear programming are basic solutions which correspond to vertices of the polyhedral feasible set X, and fundamental solutions which correspond to extreme rays of X, if X is unbounded.…”

mentioning

confidence: 99%

Connectedness of efficient solutions in multiple criteria combinatorial optimization

Ehrgott

Klamroth

1997

European Journal of Operational Research

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mentioning

confidence: 99%

Connectedness of efficient solutions in multiple criteria combinatorial optimization

Ehrgott

Klamroth

1997

European Journal of Operational Research

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“…15 (iii) Methods that generate efficient solutions and incorporate preferences a posteriori. Geoffrion (1968), 16 Evans and Steuer, 17 Zeleny, 18 Ecker et al 19 and Armand and Malivert, 20 among others, undertook the problem of finding the efficient set in MOLP.…”

Section: Classification Of Methods For Solving Molp Problemsmentioning

confidence: 99%

A multi-objective approach to determine alternative targets in data envelopment analysis

Lins

Meza

Silva

2004

Journal of the Operational Research Society

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The choice for radial projections of classic data envelopment analysis (DEA) models, resulting in a number of projections onto the Pareto-inefficient portion of the frontier, has been seen lately as a disadvantage in DEA. The search for a non-radial projection method resulted in developments such as preference structure models. These models consider a priori preference incorporation, using weights in the search for the most preferred efficient target, although presenting some implementation difficulties. In this paper, we propose a multi-objective approach that determines the bases for a posteriori preference incorporation, through individual projections of each variable (input or output) as an objective function, thus allowing one to obtain a target at every extreme-efficient point on the frontier. This multi-objective approach is shown to be equivalent to the preference structure models, yet presenting some advantages, such as the mapping of the possible weights, assigned to partial efficiencies of an observed unit, in order to reach a specific target.

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“…(The theory and a number of computer programs for multiobjective linear problems are widely discussed in the literature, e.g. by Evans and Steuer (1973) and Zeleny (1974).) In contrast, nonlinear problem formulations do not add to our understanding of reality and often cannot be solved by standard computerized solution techniques.…”

Section: The Basic Model and Difierent Versionsmentioning

confidence: 99%

Decision Support for Innovation Management: Application to the Lighting Industry.

Beaumont¹,

Haustein²,

Weber³

1985

The Journal of the Operational Research Society

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I n t e r n a t i o n a l I n s t i t u t e f o r A p p l i e d S y s t e m s A n a l y s i s , L a x e n b u r g , A u s t r i a RR-83-29 December 1983 INTERNATIONAL INSTlTUTE FOR APPLIED SYSIXMS ANALYSIS Laxenburg, AustriaInternational Standard Book Number 3-7045-0050-5Research Reports, which record research conducted a t IIASA, a r e independently reviewed before publication. However, t h e views and opinions they express a r e not necessarily those of t h e Institute or t h e National Member Organizations t h a t support it.Copyright O 1983 International Institute for Applied Systems Analysis All rights reserved. No part of this publication may be reproduced or transmitted in any form o r by any means, electronic or mechanical, including photocopy, recording, or any information storage o r retrieval system, without permission in writing from the publisher. Cover design by Anka JamesPrinted by Novographic, Vienna. Austria In today's turbulent economic environment, every decision affecting t h e development of industry necessarily carries a n increased risk t h a t t h e anticipated economic and social goals will not be achieved. The description of decision making does not always include t h e notion of risk. Sometimes t h e "volatility of cost factors" or changing economies of scale (innovation being t h e primary reason for t h e change) are held responsible for uncertainty about future development. These phenomena are also used t o explain t h e decline in capital formation and in decisions to invest t h a t we a r e currently witnessing.The economic and decision sciences are trying to cope with this situation by devising more sophisticated methods and procedures for supporting decision making. This Research Report reviews some methods t h a t a r e applicable t o t h e analysis of innovation patterns, with t h e a i m of basing t h e necessary decisions on more sound reasoning. The report t h e n describes t h e application of some of these methods to innovation management i n t h e lighting industry. I t is hoped t h a t their application will result in better decisions being made in t h e allocation of resources for innovation. DECISION SUPPORT FOR INNOVATION MANAGEMENT: APPLICATION TO THE LIGHTING INDUSTRY Heinz-Dieter Haustein and Mathias Weber h t e r n a t i o n a l I n s t i t u t e f o r Applied S y s t e m s Analysis, L a z e n b u r g , A u s t r i a Making d e c i s i o n s a b o u t r e s o u r c e a l l o c a t i o n f o r f u t u r e i n n o v a t i o n s is a c h a l l e n g i n g t a s k in b o t h p l a n n e d a n d m a r k e t e c o n o m i e s . Not o n l y c a n s u c h d e c i s i o n s n o t be r e v e r s e d w i t h o u t considerable l o s s of efficiency, b u t t h e decis i o n m a k e r g e n e r a l l y f a c e s a n u m b e r of conflicting objectives. In this r e p o r t the a u t h o r s t r y t o c o m b i n e t w o d i s c i p l i n e s that have b e e n evolving i n d e p e nd e n t l y f o r a l o n g t i m e : i n n o v a t i o n t h e o r y a n d d e c i s i o n theo...

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A revised simplex method for linear multiple objective programs

Cited by 229 publications

References 4 publications

Connectedness of efficient solutions in multiple criteria combinatorial optimization

Connectedness of efficient solutions in multiple criteria combinatorial optimization

A multi-objective approach to determine alternative targets in data envelopment analysis

Decision Support for Innovation Management: Application to the Lighting Industry.

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